Anwar Alhazmi1, Yaser Alhazmi2, Ali Makrami3, Amal Masmali4, Nourah Salawi4, Khulud Masmali4, Shankargouda Patil2. 1. Department of Preventive Dental Science, Jazan University, College of Dentistry, Jazan, Saudi Arabia. 2. Department of Maxillofacial Surgery and Diagnostic Sciences, Jazan University, College of Dentistry, Jazan, Saudi Arabia. 3. Prince Mohammed Bin Nasser Hospital, Ministry of health, Jazan, Saudi Arabia. 4. Ministry of health, Jazan, Saudi Arabia.
Abstract
BACKGROUND: Oral cancer requires early diagnosis and treatment to increase the chances of survival. This study aimed to develop an artificial neural network model that helps to predict the individuals' risk of developing oral cancer based on data on risk factors, systematic medical condition, and clinic-pathological features. METHODS: A popular data mining algorithm artificial neural network was used for developing the artificial intelligence-based prediction model. A total of 29 variables that were associated with the patients were used for developing the model. The dataset was randomly split into the training dataset 54 (75%) cases and testing dataset 19 (25%) cases. All records and observations were reviewed by Board-certified oral pathologist. RESULTS: A total of 73 patients met the eligibility criteria. Twenty-two (30.13%) were benign cases, and 51 (69.86%) were malignant cases. Thirty-seven were female, and 36 were male, with a mean age of 63.09 years. Our analysis displayed that the average sensitivity and specificity of ANN for oral cancer prediction based on the 10-fold cross-validation analysis was 85.71% (95% confidence interval [CI], 57.19-98.22) and 60.00% (95% CI, 14.66-94.73), respectively. The accuracy of ANN for oral cancer prediction was 78.95% (95% CI, 54.43-931.95). CONCLUSION: Our results suggest that this machine-learning technique has the potential to help in oral cancer screening and diagnosis based on the datasets. The results demonstrate that the artificial neural network could perform well in estimating the probability of malignancy and improve the positive predictive value that could help to predict the individuals' risk of developing OC based on knowledge of their risk factors, systemic medical conditions, and clinic-pathological data.
BACKGROUND: Oral cancer requires early diagnosis and treatment to increase the chances of survival. This study aimed to develop an artificial neural network model that helps to predict the individuals' risk of developing oral cancer based on data on risk factors, systematic medical condition, and clinic-pathological features. METHODS: A popular data mining algorithm artificial neural network was used for developing the artificial intelligence-based prediction model. A total of 29 variables that were associated with the patients were used for developing the model. The dataset was randomly split into the training dataset 54 (75%) cases and testing dataset 19 (25%) cases. All records and observations were reviewed by Board-certified oral pathologist. RESULTS: A total of 73 patients met the eligibility criteria. Twenty-two (30.13%) were benign cases, and 51 (69.86%) were malignant cases. Thirty-seven were female, and 36 were male, with a mean age of 63.09 years. Our analysis displayed that the average sensitivity and specificity of ANN for oral cancer prediction based on the 10-fold cross-validation analysis was 85.71% (95% confidence interval [CI], 57.19-98.22) and 60.00% (95% CI, 14.66-94.73), respectively. The accuracy of ANN for oral cancer prediction was 78.95% (95% CI, 54.43-931.95). CONCLUSION: Our results suggest that this machine-learning technique has the potential to help in oral cancer screening and diagnosis based on the datasets. The results demonstrate that the artificial neural network could perform well in estimating the probability of malignancy and improve the positive predictive value that could help to predict the individuals' risk of developing OC based on knowledge of their risk factors, systemic medical conditions, and clinic-pathological data.
Authors: Atta-Ur Rahman; Abdullah Alqahtani; Nahier Aldhafferi; Muhammad Umar Nasir; Muhammad Farhan Khan; Muhammad Adnan Khan; Amir Mosavi Journal: Sensors (Basel) Date: 2022-05-18 Impact factor: 3.847
Authors: Shankargouda Patil; Sarah Albogami; Jagadish Hosmani; Sheetal Mujoo; Mona Awad Kamil; Manawar Ahmad Mansour; Hina Naim Abdul; Shilpa Bhandi; Shiek S S J Ahmed Journal: Diagnostics (Basel) Date: 2022-04-19
Authors: Mohanad A Deif; Hani Attar; Ayman Amer; Ismail A Elhaty; Mohammad R Khosravi; Ahmed A A Solyman Journal: Comput Intell Neurosci Date: 2022-09-30